{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer mini-batch_filter-data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/experiment/mini-batch_filter-data.ipynb)\n",
    "\n",
    "↑ Click the above button to open the optimizer on Google Colab.\n",
    "\n",
    "> If you can't see the button and are located in the Chinese Mainland, please use a proxy or VPN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your **Anki Deck Package (.apkg)** file or **Anki Collection Package (.colpkg)** file on the `Left sidebar -> Files`, drag and drop your file in the current directory (not the `sample_data` directory). \n",
    "\n",
    "No need to include media. Need to include scheduling information. \n",
    "\n",
    "> If you use the latest version of Anki, please check the box `Support older Anki versions (slower/larger files)` when you export.\n",
    "\n",
    "You can export it via `File -> Export...` or `Ctrl + E` in the main window of Anki.\n",
    "\n",
    "Then replace the `filename` with yours in the next code cell. And set the `timezone` and `next_day_starts_at` which can be found in your preferences of Anki.\n",
    "\n",
    "After that, just run all (`Runtime -> Run all` or `Ctrl + F9`) and wait for minutes. You can see the optimal parameters in section **2.3 Result**. Copy them, replace the parameters in `fsrs4anki_scheduler.js`, and paste them into the custom scheduling of your deck options (require Anki version >= 2.1.55).\n",
    "\n",
    "**NOTE**: The default output is generated from my review logs. If you find the output is the same as mine, maybe your notebook hasn't run there.\n",
    "\n",
    "**Contribute to SRS Research**: If you want to share your data with me, please fill this form: https://forms.gle/KaojsBbhMCytaA7h8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Build dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Extract Anki collection & deck file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract successfully!\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "from tqdm import notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import timedelta, datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import sys\n",
    "import torch\n",
    "from torch import nn\n",
    "from sklearn.utils import shuffle\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'./{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Extract successfully!\")\n",
    "\n",
    "\n",
    "notebook.tqdm.pandas()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Create time-series feature & analysis\n",
    "\n",
    "The following code cell will extract the review logs from your Anki collection and preprocess them to a trainset which is saved in [./revlog_history.tsv](./revlog_history.tsv).\n",
    "\n",
    "The time-series features are important in optimizing the model's parameters. For more detail, please see my paper: https://www.maimemo.com/paper/\n",
    "\n",
    "Then it will generate a concise analysis for your review logs. \n",
    "\n",
    "- The `r_history` is the history of ratings on each review. `3,3,3,1` means that you press `Good, Good, Good, Again`. It only contains the first rating for each card on the review date, i.e., when you press `Again` in review and  `Good` in relearning steps 10min later, only `Again` will be recorded.\n",
    "- The `avg_interval` is the actual average interval after you rate your cards as the `r_history`. It could be longer than the interval given by Anki's built-in scheduler because you reviewed some overdue cards.\n",
    "- The `avg_retention` is the average retention after you press as the `r_history`. `Again` counts as failed recall, and `Hard, Good and Easy` count as successful recall. Retention is the percentage of your successful recall.\n",
    "- The `stability` is the estimated memory state variable, which is an approximate interval that leads to 90% retention.\n",
    "- The `factor` is `stability / previous stability`.\n",
    "- The `group_cnt` is the number of review logs that have the same `r_history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "revlog.csv saved.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa90904a09b04fe69269f8386f57aa71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/30711 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7bcd4d112ce643a99e5e354e5a2dcf5d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/114727 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee66a6e658974cbb8b1be4d5a3b5b57d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/30686 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainset saved.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da2c480c78794998bbc47850ee3a6a38",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/71753 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retention calculated.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4313914a2e3b4ff5b9914867dff4ac3f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/49911 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b7fc1bba1d84912811ea03f6594fb97",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/939 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.0          0.763        0.4     inf       2902\n",
      "            1,3           3.0          0.862        2.2    5.50       1633\n",
      "          1,3,3           7.0          0.863        5.0    2.27       1051\n",
      "        1,3,3,3          16.1          0.853       10.2    2.04        659\n",
      "      1,3,3,3,3          36.4          0.823       18.2    1.78        353\n",
      "    1,3,3,3,3,3          84.0          0.868       27.3    1.50        174\n",
      "              2           1.0          0.899        1.0     inf        188\n",
      "            2,3           3.5          0.936        8.0    8.00        157\n",
      "          2,3,3          12.0          0.858        4.2    0.52        118\n",
      "              3           1.0          0.969        3.3     inf       4485\n",
      "            3,3           3.1          0.966        9.7    2.94       2681\n",
      "          3,3,3           7.1          0.965       20.5    2.11       2258\n",
      "        3,3,3,3          16.8          0.946       32.9    1.60       1725\n",
      "      3,3,3,3,3          37.6          0.932       51.3    1.56        997\n",
      "    3,3,3,3,3,3          84.0          0.929      108.4    2.11        599\n",
      "  3,3,3,3,3,3,3         134.2          0.954      128.0    1.18        279\n",
      "              4           3.8          0.912        4.6     inf      19751\n",
      "            4,3           8.2          0.955       22.1    4.80      11419\n",
      "          4,3,3          18.8          0.948       42.4    1.92       7604\n",
      "        4,3,3,3          34.9          0.933       68.4    1.61       4070\n",
      "      4,3,3,3,3          50.3          0.923       89.3    1.31       1696\n",
      "    4,3,3,3,3,3          78.4          0.933       89.6    1.00        593\n",
      "  4,3,3,3,3,3,3          89.1          0.968       68.9    0.77        258\n",
      "4,3,3,3,3,3,3,3         119.1          0.984      188.3    2.73        164\n",
      "Analysis saved!\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(\"SELECT * FROM revlog\")\n",
    "revlog = res.fetchall()\n",
    "\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl',\n",
    "              'last_ivl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000) &\n",
    "        (df['r'] > 0)].copy()\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "df = df[df['type'] != 3].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=next_day_starts_at)\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "\n",
    "# Remove `again` in `relearning` stage.\n",
    "df = df.drop(df[(df['type'] == 2) & (df['r'] == 1)].index)\n",
    "# Keep `easy` as the first rating when `easy` is in `learning` stage.\n",
    "cid_with_r_4 = df[(df['type'] == 0) & (df['r'] == 4)]['cid'].unique()\n",
    "df1 = df[(df['cid'].isin(cid_with_r_4)) & (df['type'] == 0) & (df['r'] == 4)]\n",
    "df2 = df[~((df['cid'].isin(cid_with_r_4)) & (df['type'] == 0) & (df['r'] != 4))]\n",
    "df = pd.concat([df1, df2])\n",
    "\n",
    "\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['delta_t'] = df['delta_t'].astype(dtype=int)\n",
    "df['i'] = 1\n",
    "df['r_history'] = \"\"\n",
    "df['t_history'] = \"\"\n",
    "col_idx = {key: i for i, key in enumerate(df.columns)}\n",
    "\n",
    "\n",
    "# code from https://github.com/L-M-Sherlock/anki_revlog_analysis/blob/main/revlog_analysis.py\n",
    "def get_feature(x):\n",
    "    last_kind = None\n",
    "    for idx, log in enumerate(x.itertuples()):\n",
    "        if last_kind is not None and last_kind in (1, 2) and log.type == 0:\n",
    "            return x.iloc[:idx]\n",
    "        last_kind = log.type\n",
    "        if idx == 0:\n",
    "            if log.type != 0:\n",
    "                return x.iloc[:idx]\n",
    "            x.iloc[idx, col_idx['delta_t']] = 0\n",
    "        if idx == x.shape[0] - 1:\n",
    "            break\n",
    "        x.iloc[idx + 1, col_idx['i']] = x.iloc[idx, col_idx['i']] + 1\n",
    "        x.iloc[idx + 1, col_idx['t_history']] = f\"{x.iloc[idx, col_idx['t_history']]},{x.iloc[idx, col_idx['delta_t']]}\"\n",
    "        x.iloc[idx + 1, col_idx['r_history']] = f\"{x.iloc[idx, col_idx['r_history']]},{x.iloc[idx, col_idx['r']]}\"\n",
    "    return x\n",
    "\n",
    "df = df.groupby('cid', as_index=False, group_keys=False).progress_apply(get_feature)\n",
    "\n",
    "def remove_outliers(sub_df):\n",
    "    Q1 = sub_df['delta_t'].quantile(0.25)\n",
    "    Q3 = sub_df['delta_t'].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    return sub_df[(sub_df['delta_t'] >= Q1 - 1.5 * IQR) & (sub_df['delta_t'] <= Q3 + 1.5 * IQR)]\n",
    "\n",
    "df = df.groupby(['r_history', 't_history'], as_index=False, group_keys=False).progress_apply(remove_outliers)\n",
    "\n",
    "# Remove super-overdue reviews.\n",
    "df['last_ivl'] = df['last_ivl'].map(lambda x: 1 if x < 0 else x)\n",
    "df['overdue_rate'] = df['delta_t'] / df['last_ivl']\n",
    "df = df[df['overdue_rate'] < 4]\n",
    "del df['overdue_rate']\n",
    "\n",
    "def remove_non_continuous_rows(group):\n",
    "    discontinuity = group['i'].diff().fillna(1).ne(1)\n",
    "    if not discontinuity.any():\n",
    "        return group\n",
    "    else:\n",
    "        first_non_continuous_index = discontinuity.idxmax()\n",
    "        return group.loc[:first_non_continuous_index-1]\n",
    "\n",
    "df = df.groupby('cid', as_index=False, group_keys=False).progress_apply(remove_non_continuous_rows)\n",
    "\n",
    "df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000]\n",
    "df[\"t_history\"] = df[\"t_history\"].map(lambda x: x[1:] if len(x) > 1 else x)\n",
    "df[\"r_history\"] = df[\"r_history\"].map(lambda x: x[1:] if len(x) > 1 else x)\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "def cal_retention(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group['retention'] = round(group['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x]).mean(), 4)\n",
    "    group['total_cnt'] = group.shape[0]\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history', 'delta_t'], group_keys=False).progress_apply(cal_retention)\n",
    "print(\"Retention calculated.\")\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_ivl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in notebook.tqdm(df.index):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)]\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "    print(df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False))\n",
    "    print(\"Analysis saved!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Optimize parameter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define the model\n",
    "\n",
    "FSRS is a time-series model for predicting memory states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 5, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2, 0.2, 0.2, 1]\n",
    "'''\n",
    "w[0]: initial_stability_for_again_answer\n",
    "w[1]: initial_stability_step_per_rating\n",
    "w[2]: initial_difficulty_for_good_answer\n",
    "w[3]: initial_difficulty_step_per_rating\n",
    "w[4]: next_difficulty_step_per_rating\n",
    "w[5]: next_difficulty_reversion_to_mean_speed (used to avoid ease hell)\n",
    "w[6]: next_stability_factor_after_success\n",
    "w[7]: next_stability_stabilization_decay_after_success\n",
    "w[8]: next_stability_retrievability_gain_after_success\n",
    "w[9]: next_stability_factor_after_failure\n",
    "w[10]: next_stability_difficulty_decay_after_success\n",
    "w[11]: next_stability_stability_gain_after_failure\n",
    "w[12]: next_stability_retrievability_gain_after_failure\n",
    "For more details about the parameters, please see: \n",
    "https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler\n",
    "'''\n",
    "\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w))\n",
    "\n",
    "    def stability_after_success(self, state, new_d, r):\n",
    "        new_s = state[:,0] * (1 + torch.exp(self.w[6]) *\n",
    "                        (11 - new_d) *\n",
    "                        torch.pow(state[:,0], -self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "    \n",
    "    def stability_after_failure(self, state, new_d, r):\n",
    "        new_s = self.w[9] * torch.pow(new_d, -self.w[10]) * torch.pow(\n",
    "            state[:,0], self.w[11]) * torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def step(self, X, state):\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X[:,1] - 1)\n",
    "            new_d = self.w[2] - self.w[3] * (X[:,1] - 3)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "        else:\n",
    "            r = torch.exp(np.log(0.9) * X[:,0] / state[:,0])\n",
    "            new_d = state[:,1] - self.w[4] * (X[:,1] - 3)\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state, new_d, r), self.stability_after_failure(state, new_d, r))\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs, state=None):\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        else:\n",
    "            state, = state\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init, current):\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "\n",
    "class WeightClipper(object):\n",
    "    def __init__(self, frequency=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, 10)\n",
    "            w[3] = w[3].clamp(0.1, 5)\n",
    "            w[4] = w[4].clamp(0.1, 5)\n",
    "            w[5] = w[5].clamp(0, 0.5)\n",
    "            w[6] = w[6].clamp(0, 2)\n",
    "            w[7] = w[7].clamp(0.01, 0.2)\n",
    "            w[8] = w[8].clamp(0.01, 1.5)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(0.01, 2)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 3)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line):\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from typing import List\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe):\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "    \n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source, batch_size):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0:\n",
    "            self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from [self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist()]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Optimizer(object):\n",
    "    def __init__(self, train_set, test_set, n_epoch=1, lr=1e-2, batch_size=256) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set, test_set):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self):\n",
    "        # pretrain\n",
    "        self.eval()\n",
    "        pbar = notebook.tqdm(desc=\"pre-train\", colour=\"red\", total=len(self.pre_train_data_loader))\n",
    "\n",
    "        for i, batch in enumerate(self.pre_train_data_loader):\n",
    "            self.model.train()\n",
    "            self.optimizer.zero_grad()\n",
    "            sequences, delta_ts, labels, seq_lens = batch\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences)\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            loss = self.loss_fn(retentions, labels)\n",
    "            loss.backward()\n",
    "            self.optimizer.step()\n",
    "            self.model.apply(self.clipper)\n",
    "            pbar.update(n=real_batch_size)\n",
    "\n",
    "        pbar.close()\n",
    "        for name, param in self.model.named_parameters():\n",
    "            print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "\n",
    "        epoch_len = len(self.next_train_data_loader)\n",
    "        pbar = notebook.tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "        print_len = max(self.batch_nums*self.n_epoch // 10, 1)\n",
    "\n",
    "\n",
    "        for k in range(self.n_epoch):\n",
    "            self.eval()\n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "\n",
    "                if (k * self.batch_nums + i + 1) % print_len == 0:\n",
    "                    print(f\"iteration: {k * epoch_len + (i + 1) * self.batch_size}\")\n",
    "                    for name, param in self.model.named_parameters():\n",
    "                        print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "                \n",
    "        self.eval()\n",
    "\n",
    "        w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "        return w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sampler = RevlogSampler(self.train_set, batch_size=4096)\n",
    "            train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "            loss = 0\n",
    "            for batch in train_data_loader:\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss += self.loss_fn(retentions, labels)\n",
    "            self.avg_train_losses.append(loss/len(train_data_loader))\n",
    "            print(f\"Loss in trainset: {self.avg_train_losses[-1]:.4f}\")\n",
    "\n",
    "            sampler = RevlogSampler(self.test_set, batch_size=4096)\n",
    "            test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "            loss = 0\n",
    "            for batch in test_data_loader:\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss += self.loss_fn(retentions, labels)\n",
    "            self.avg_eval_losses.append(loss/len(test_data_loader))\n",
    "            print(f\"Loss in testset: {self.avg_eval_losses[-1]:.4f}\")\n",
    "\n",
    "    def plot(self):\n",
    "        plt.plot(self.avg_train_losses, label='train')\n",
    "        plt.plot(self.avg_eval_losses, label='test')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.ylabel('loss')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Train the model\n",
    "\n",
    "The [./revlog_history.tsv](./revlog_history.tsv) generated before will be used for training the FSRS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd7857f9839c4163bb05f0e1ff4d61c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/185678 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/fsrs4anki/lib/python3.8/site-packages/sklearn/model_selection/_split.py:885: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 113202 TEST: 72476\n",
      "dataset built\n",
      "Loss in trainset: 0.3362\n",
      "Loss in testset: 0.3184\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d08f26b70c1403783a53c2f70a57f33",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/7575 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [0.5356, 1.0562, 5.0, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8818264c92764c81a8263e89d927aac1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/316881 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3343\n",
      "Loss in testset: 0.3187\n",
      "iteration: 31488\n",
      "w: [0.4169, 1.3073, 5.344, 1.3694, 1.4586, 0.0062, 1.4303, 0.0476, 0.9005, 1.6725, 0.5528, 0.6134, 0.946]\n",
      "iteration: 62976\n",
      "w: [0.4169, 1.3073, 5.1121, 1.3925, 1.4095, 0.0, 1.3304, 0.0166, 0.8227, 1.642, 0.5677, 0.69, 0.9144]\n",
      "iteration: 94464\n",
      "w: [0.4169, 1.3073, 4.9791, 1.4173, 1.5553, 0.0479, 1.2904, 0.1507, 0.8057, 1.6256, 0.5681, 0.5312, 0.9637]\n",
      "Loss in trainset: 0.3129\n",
      "Loss in testset: 0.3040\n",
      "iteration: 125851\n",
      "w: [0.4169, 1.3073, 4.8419, 1.461, 1.5776, 0.0, 1.295, 0.1095, 0.8107, 1.7229, 0.4533, 0.5921, 0.9559]\n",
      "iteration: 157339\n",
      "w: [0.4169, 1.3073, 4.8437, 1.4004, 1.7214, 0.0464, 1.3288, 0.1481, 0.8611, 1.6546, 0.4888, 0.6116, 0.9663]\n",
      "iteration: 188827\n",
      "w: [0.4169, 1.3073, 4.7705, 1.4359, 1.722, 0.0063, 1.3134, 0.1218, 0.8436, 1.6673, 0.4684, 0.5605, 0.9691]\n",
      "Loss in trainset: 0.3125\n",
      "Loss in testset: 0.3036\n",
      "iteration: 220214\n",
      "w: [0.4169, 1.3073, 4.7838, 1.3376, 1.7293, 0.0028, 1.297, 0.1074, 0.8231, 1.6402, 0.4897, 0.5999, 0.9015]\n",
      "iteration: 251702\n",
      "w: [0.4169, 1.3073, 4.7754, 1.3542, 1.7273, 0.0034, 1.3104, 0.1121, 0.8304, 1.657, 0.4651, 0.6458, 0.892]\n",
      "iteration: 283190\n",
      "w: [0.4169, 1.3073, 4.7744, 1.369, 1.7208, 0.0145, 1.3187, 0.1023, 0.8366, 1.6498, 0.4699, 0.6292, 0.8859]\n",
      "iteration: 314678\n",
      "w: [0.4169, 1.3073, 4.7713, 1.3633, 1.7203, 0.0085, 1.315, 0.1045, 0.8325, 1.6505, 0.4686, 0.631, 0.8876]\n",
      "Loss in trainset: 0.3118\n",
      "Loss in testset: 0.3032\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 128416 TEST: 57262\n",
      "dataset built\n",
      "Loss in trainset: 0.3355\n",
      "Loss in testset: 0.3152\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "312d78aff4e04998aaaec491347cd6f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/22841 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [0.5367, 1.216, 5.0, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "762a58b5265644f89b27ac76f0153bdd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/316725 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3338\n",
      "Loss in testset: 0.3150\n",
      "iteration: 31488\n",
      "w: [0.4421, 1.1615, 5.1603, 1.2466, 1.8997, 0.0928, 1.4741, 0.0992, 0.9625, 1.7833, 0.4599, 0.5605, 1.1864]\n",
      "iteration: 62976\n",
      "w: [0.4421, 1.1615, 5.1362, 1.4633, 1.8555, 0.0029, 1.3642, 0.1274, 0.9291, 1.7492, 0.5206, 0.5405, 1.2753]\n",
      "iteration: 94464\n",
      "w: [0.4421, 1.1615, 4.8448, 1.4528, 1.9243, 0.0335, 1.3079, 0.1018, 0.912, 1.6936, 0.6045, 0.5527, 1.1652]\n",
      "Loss in trainset: 0.3147\n",
      "Loss in testset: 0.2960\n",
      "iteration: 125799\n",
      "w: [0.4421, 1.1615, 4.8507, 1.7083, 1.988, 0.0194, 1.3854, 0.0917, 1.0003, 1.6935, 0.584, 0.6246, 1.0692]\n",
      "iteration: 157287\n",
      "w: [0.4421, 1.1615, 4.7382, 1.55, 1.9222, 0.0078, 1.2705, 0.1656, 0.9068, 1.7562, 0.5105, 0.6819, 1.1268]\n",
      "iteration: 188775\n",
      "w: [0.4421, 1.1615, 4.6698, 1.5404, 1.8974, 0.0255, 1.292, 0.1074, 0.9288, 1.716, 0.5336, 0.5798, 1.1342]\n",
      "Loss in trainset: 0.3155\n",
      "Loss in testset: 0.2966\n",
      "iteration: 220110\n",
      "w: [0.4421, 1.1615, 4.763, 1.5037, 1.9791, 0.0003, 1.2448, 0.1391, 0.8722, 1.7482, 0.493, 0.6014, 1.1382]\n",
      "iteration: 251598\n",
      "w: [0.4421, 1.1615, 4.7486, 1.4702, 2.0014, 0.0194, 1.2621, 0.12, 0.8859, 1.7282, 0.5052, 0.6131, 1.109]\n",
      "iteration: 283086\n",
      "w: [0.4421, 1.1615, 4.7096, 1.4477, 1.9847, 0.0069, 1.2866, 0.0916, 0.9076, 1.7309, 0.5012, 0.6193, 1.096]\n",
      "iteration: 314574\n",
      "w: [0.4421, 1.1615, 4.7171, 1.4557, 1.9894, 0.0077, 1.2801, 0.0942, 0.9006, 1.7246, 0.5068, 0.6232, 1.0911]\n",
      "Loss in trainset: 0.3144\n",
      "Loss in testset: 0.2955\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 129738 TEST: 55940\n",
      "dataset built\n",
      "Loss in trainset: 0.3170\n",
      "Loss in testset: 0.3577\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1a6048e96174a83b8d7470a1cf73127",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/24236 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.0066, 1.0064, 5.0, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13c6224a0f61466c9285202f3b00a7aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/316506 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3170\n",
      "Loss in testset: 0.3578\n",
      "iteration: 31488\n",
      "w: [0.9045, 0.9018, 5.2259, 1.357, 1.4512, 0.1173, 1.2923, 0.0317, 0.8717, 1.5881, 0.6024, 0.534, 0.9949]\n",
      "iteration: 62976\n",
      "w: [0.9045, 0.9018, 4.9747, 1.3067, 1.4308, 0.0107, 1.3493, 0.1176, 0.9816, 1.7027, 0.517, 0.5567, 1.0111]\n",
      "iteration: 94464\n",
      "w: [0.9045, 0.9018, 4.9011, 1.1953, 1.5202, 0.0, 1.1613, 0.1056, 0.8048, 1.7279, 0.5128, 0.6858, 1.1266]\n",
      "Loss in trainset: 0.3004\n",
      "Loss in testset: 0.3337\n",
      "iteration: 125726\n",
      "w: [0.9045, 0.9018, 4.7195, 1.3208, 1.7261, 0.037, 1.2244, 0.1307, 0.8953, 1.6802, 0.5543, 0.6323, 0.751]\n",
      "iteration: 157214\n",
      "w: [0.9045, 0.9018, 4.7443, 1.415, 1.8656, 0.0122, 1.1752, 0.0841, 0.8484, 1.7035, 0.4938, 0.6293, 0.7696]\n",
      "iteration: 188702\n",
      "w: [0.9045, 0.9018, 4.7082, 1.2468, 1.9038, 0.0073, 1.2296, 0.0688, 0.9038, 1.6754, 0.5056, 0.6279, 0.7879]\n",
      "Loss in trainset: 0.2999\n",
      "Loss in testset: 0.3324\n",
      "iteration: 219964\n",
      "w: [0.9045, 0.9018, 4.7825, 1.5224, 1.856, 0.0178, 1.2023, 0.0792, 0.8681, 1.6654, 0.5081, 0.5847, 0.8242]\n",
      "iteration: 251452\n",
      "w: [0.9045, 0.9018, 4.7553, 1.4746, 1.8567, 0.0241, 1.2279, 0.0692, 0.8895, 1.6795, 0.4849, 0.623, 0.8285]\n",
      "iteration: 282940\n",
      "w: [0.9045, 0.9018, 4.7478, 1.4813, 1.8601, 0.0046, 1.2277, 0.0754, 0.8872, 1.6892, 0.4731, 0.6347, 0.8254]\n",
      "iteration: 314428\n",
      "w: [0.9045, 0.9018, 4.7407, 1.4676, 1.8583, 0.0075, 1.2283, 0.0758, 0.8876, 1.6961, 0.4661, 0.6475, 0.8325]\n",
      "Loss in trainset: 0.2999\n",
      "Loss in testset: 0.3325\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['y'] = dataset['r'].map({1: 0, 2: 1, 3: 1, 4: 1})\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "\n",
    "w = []\n",
    "\n",
    "sgkf = StratifiedGroupKFold(n_splits=3)\n",
    "for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "    train_set = dataset.iloc[train_index].copy()\n",
    "    test_set = dataset.iloc[test_index].copy()\n",
    "    optimizer = Optimizer(train_set, test_set, n_epoch=3, lr=4e-2, batch_size=256)\n",
    "    w.append(optimizer.train())\n",
    "    optimizer.plot()\n",
    "\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Result\n",
    "\n",
    "Copy the optimal parameters for FSRS for you in the output of next code cell after running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5878, 1.1235, 4.743, 1.4289, 1.856, 0.0079, 1.2745, 0.0915, 0.8736, 1.6904, 0.4805, 0.6339, 0.9371]\n"
     ]
    }
   ],
   "source": [
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "print(avg_w.tolist())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Preview\n",
    "\n",
    "You can see the memory states and intervals generated by FSRS as if you press the good in each review at the due date scheduled by FSRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,1,2,4,8,15,29,53,96,170,295\n",
      "difficulty history: 0,7.6,7.6,7.6,7.5,7.5,7.5,7.5,7.4,7.4,7.4\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,2,5,12,27,59,123,250,492,940,1748\n",
      "difficulty history: 0,6.2,6.2,6.1,6.1,6.1,6.1,6.1,6.1,6.1,6.1\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,3,8,22,56,135,311,687,1459,2989,5926\n",
      "difficulty history: 0,4.7,4.7,4.7,4.7,4.7,4.7,4.7,4.7,4.7,4.7\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,4,13,39,108,284,706,1670,3774,8181,17069\n",
      "difficulty history: 0,3.3,3.3,3.3,3.3,3.4,3.4,3.4,3.4,3.4,3.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w):\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, t_history, r_history):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "        \n",
    "    def batch_predict(self, dataset):\n",
    "        fast_dataset = RevlogDataset(dataset)\n",
    "        outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "        stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "        return stabilities.tolist(), difficulties.tolist()\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "for first_rating in (1,2,3,4):\n",
    "    print(f'first rating: {first_rating}')\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = max(round(float(np.log(requestRetention)/np.log(0.9) * states[0])), 1)\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    print(f\"rating history: {r_history}\")\n",
    "    print(f\"interval history: {t_history}\")\n",
    "    print(f\"difficulty history: {d_history}\")\n",
    "    print('')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the `test_rating_sequence` to see the scheduling intervals in different ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.8348, 4.7430])\n",
      "tensor([8.4036, 4.7430])\n",
      "tensor([21.8622,  4.7430])\n",
      "tensor([55.7551,  4.7430])\n",
      "tensor([134.9468,   4.7430])\n",
      "tensor([14.9369,  8.4257])\n",
      "tensor([ 3.4097, 10.0000])\n",
      "tensor([4.3227, 9.9585])\n",
      "tensor([5.5605, 9.9173])\n",
      "tensor([7.4406, 9.8764])\n",
      "tensor([9.6564, 9.8358])\n",
      "tensor([12.8512,  9.7956])\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,3,8,22,56,135,15,3,4,6,7,10,13\n",
      "difficulty history: 0,4.7,4.7,4.7,4.7,4.7,8.4,10.0,10.0,9.9,9.9,9.8,9.8\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "easyBonus = 1.3\n",
    "hardInterval = 1.2\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = max(1,round(float(np.log(requestRetention)/np.log(0.9) * states[0])))\n",
    "    if rating == '4':\n",
    "        next_t = round(next_t * easyBonus)\n",
    "    elif rating == '2':\n",
    "        next_t = round(last_t * hardInterval)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "print(f\"rating history: {test_rating_sequence}\")\n",
    "print(f\"interval history: {t_history}\")\n",
    "print(f\"difficulty history: {d_history}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Predict memory states and distribution of difficulty\n",
    "\n",
    "Predict memory states for each review group and save them in [./prediction.tsv](./prediction.tsv).\n",
    "\n",
    "Meanwhile, it will count the distribution of difficulty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction.tsv saved.\n",
      "difficulty\n",
      "1     0.035298\n",
      "2     0.033429\n",
      "3     0.268847\n",
      "4     0.001820\n",
      "5     0.141875\n",
      "6     0.010890\n",
      "7     0.086602\n",
      "8     0.071231\n",
      "9     0.044841\n",
      "10    0.305168\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "stabilities = map(lambda x: round(x, 2), stabilities)\n",
    "difficulties = map(lambda x: round(x, 2), difficulties)\n",
    "dataset['stability'] = list(stabilities)\n",
    "dataset['difficulty'] = list(difficulties)\n",
    "prediction = dataset.groupby(by=['t_history', 'r_history']).agg({\"stability\": \"mean\", \"difficulty\": \"mean\", \"id\": \"count\"})\n",
    "prediction.reset_index(inplace=True)\n",
    "prediction.sort_values(by=['r_history'], inplace=True)\n",
    "prediction.rename(columns={\"id\": \"count\"}, inplace=True)\n",
    "prediction.to_csv(\"./prediction.tsv\", sep='\\t', index=None)\n",
    "print(\"prediction.tsv saved.\")\n",
    "prediction['difficulty'] = prediction['difficulty'].map(lambda x: int(round(x)))\n",
    "difficulty_distribution = prediction.groupby(by=['difficulty'])['count'].sum() / prediction['count'].sum()\n",
    "print(difficulty_distribution)\n",
    "difficulty_distribution_padding = np.zeros(10)\n",
    "for i in range(10):\n",
    "    if i+1 in difficulty_distribution.index:\n",
    "        difficulty_distribution_padding[i] = difficulty_distribution.loc[i+1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Optimize retention to minimize the time of reviews\n",
    "\n",
    "Calculate the optimal retention to minimize the time for long-term memory consolidation. It is an experimental feature. You can use the simulator to get more accurate results:\n",
    "\n",
    "https://github.com/open-spaced-repetition/fsrs4anki/blob/main/fsrs4anki_simulator.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average time for failed cards: 25.0s\n",
      "average time for recalled cards: 8.0s\n",
      "terminal stability:  100.18\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1448a3898fa246c883b12329d100ef7e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected_time.csv saved.\n",
      "\n",
      "-----suggested retention (experimental): 0.80-----\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = 1.01\n",
    "index_len = 664\n",
    "index_offset = 200\n",
    "d_range = 10\n",
    "d_offset = 1\n",
    "r_time = 8\n",
    "f_time = 25\n",
    "max_time = 200000\n",
    "\n",
    "type_block = dict()\n",
    "type_count = dict()\n",
    "type_time = dict()\n",
    "last_t = type_sequence[0]\n",
    "type_block[last_t] = 1\n",
    "type_count[last_t] = 1\n",
    "type_time[last_t] = time_sequence[0]\n",
    "for i,t in enumerate(type_sequence[1:]):\n",
    "    type_count[t] = type_count.setdefault(t, 0) + 1\n",
    "    type_time[t] = type_time.setdefault(t, 0) + time_sequence[i]\n",
    "    if t != last_t:\n",
    "        type_block[t] = type_block.setdefault(t, 0) + 1\n",
    "    last_t = t\n",
    "\n",
    "r_time = round(type_time[1]/type_count[1]/1000, 1)\n",
    "\n",
    "if 2 in type_count and 2 in type_block:\n",
    "    f_time = round(type_time[2]/type_block[2]/1000 + r_time, 1)\n",
    "\n",
    "print(f\"average time for failed cards: {f_time}s\")\n",
    "print(f\"average time for recalled cards: {r_time}s\")\n",
    "\n",
    "def stability2index(stability):\n",
    "    return int(round(np.log(stability) / np.log(base)) + index_offset)\n",
    "\n",
    "def init_stability(d):\n",
    "    return max(((d - avg_w[2]) / -avg_w[3] + 2) * avg_w[1] + avg_w[0], np.power(base, -index_offset))\n",
    "\n",
    "def cal_next_recall_stability(s, r, d, response):\n",
    "    if response == 1:\n",
    "        return s * (1 + np.exp(avg_w[6]) * (11 - d) * np.power(s, -avg_w[7]) * (np.exp((1 - r) * avg_w[8]) - 1))\n",
    "    else:\n",
    "        return avg_w[9] * np.power(d, -avg_w[10]) * np.power(s, avg_w[11]) * np.exp((1 - r) * avg_w[12])\n",
    "\n",
    "\n",
    "stability_list = np.array([np.power(base, i - index_offset) for i in range(index_len)])\n",
    "print(f\"terminal stability: {stability_list.max(): .2f}\")\n",
    "df = pd.DataFrame(columns=[\"retention\", \"difficulty\", \"time\"])\n",
    "\n",
    "for percentage in notebook.tqdm(range(96, 66, -2)):\n",
    "    recall = percentage / 100\n",
    "    time_list = np.zeros((d_range, index_len))\n",
    "    time_list[:,:-1] = max_time\n",
    "    for d in range(d_range, 0, -1):\n",
    "        s0 = init_stability(d)\n",
    "        s0_index = stability2index(s0)\n",
    "        diff = max_time\n",
    "        while diff > 0.1:\n",
    "            s0_time = time_list[d - 1][s0_index]\n",
    "            for s_index in range(index_len - 2, -1, -1):\n",
    "                stability = stability_list[s_index]\n",
    "                interval = max(1, round(stability * np.log(recall) / np.log(0.9)))\n",
    "                p_recall = np.power(0.9, interval / stability)\n",
    "                recall_s = cal_next_recall_stability(stability, p_recall, d, 1)\n",
    "                forget_d = min(d + d_offset, 10)\n",
    "                forget_s = cal_next_recall_stability(stability, p_recall, forget_d, 0)\n",
    "                recall_s_index = min(stability2index(recall_s), index_len - 1)\n",
    "                forget_s_index = min(max(stability2index(forget_s), 0), index_len - 1)\n",
    "                recall_time = time_list[d - 1][recall_s_index] + r_time\n",
    "                forget_time = time_list[forget_d - 1][forget_s_index] + f_time\n",
    "                exp_time = p_recall * recall_time + (1.0 - p_recall) * forget_time\n",
    "                if exp_time < time_list[d - 1][s_index]:\n",
    "                    time_list[d - 1][s_index] = exp_time\n",
    "            diff = s0_time - time_list[d - 1][s0_index]\n",
    "        df.loc[0 if pd.isnull(df.index.max()) else df.index.max() + 1] = [recall, d, s0_time]\n",
    "\n",
    "df.sort_values(by=[\"difficulty\", \"retention\"], inplace=True)\n",
    "df.to_csv(\"./expected_time.csv\", index=False)\n",
    "print(\"expected_time.csv saved.\")\n",
    "\n",
    "optimal_retention_list = np.zeros(10)\n",
    "for d in range(1, d_range+1):\n",
    "    retention = df[df[\"difficulty\"] == d][\"retention\"]\n",
    "    time = df[df[\"difficulty\"] == d][\"time\"]\n",
    "    optimal_retention = retention.iat[time.argmin()]\n",
    "    optimal_retention_list[d-1] = optimal_retention\n",
    "    plt.plot(retention, time, label=f\"d={d}, r={optimal_retention}\")\n",
    "print(f\"\\n-----suggested retention (experimental): {np.inner(difficulty_distribution_padding, optimal_retention_list):.2f}-----\")\n",
    "plt.ylabel(\"expected time (second)\")\n",
    "plt.xlabel(\"retention\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Evaluate the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Loss\n",
    "\n",
    "Evaluate the model with the log loss. It will compare the log loss between initial model and trained model.\n",
    "\n",
    "And it will predict the stability, difficulty and retrievability for each revlog in [./evaluation.tsv](./evaluation.tsv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss before training: 0.3293\n",
      "Loss after training: 0.3088\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss before training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss after training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Calibration graph\n",
    "\n",
    "1. FSRS predicts the stability and retention for each review.\n",
    "2. Reviews are grouped into 40 bins according to their predicted retention.\n",
    "3. Count the true retention of each bin.\n",
    "4. Plot (predicted retention, true retention) in the line graph.\n",
    "5. Plot (predicted retention, size of bin) in the bar graph.\n",
    "6. Combine these graphs to create the calibration graph.\n",
    "\n",
    "Ideally, the blue line should be aligned with the orange one. If the blue line is higher than the orange line, the FSRS underestimates the retention. When the size of reviews within a bin is small, actual retention may deviate largely, which is normal.\n",
    "\n",
    "R-squared (aka the coefficient of determination), is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). The higher the R-squared, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Coefficient_of_determination\n",
    "\n",
    "RMSE (root mean squared error) is the square root of the average of squared differences between prediction and actual observation. The lower the RMSE, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Root-mean-square_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9255\n",
      "RMSE: 0.0152\n",
      "[0.11750929 0.86894948]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = np.sqrt(mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts))\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    fit_wls = sm.WLS(bin_correct_means, sm.add_constant(bin_prediction_means), weights=bin_counts).fit()\n",
    "    print(fit_wls.params)\n",
    "    y_siegel = [fit_wls.params[0] + fit_wls.params[1]*x for x in bin_prediction_means]\n",
    "    plt.plot(bin_prediction_means, y_siegel, label='Siegel Repeated Median Regression', color=\"green\")\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration', color=\"#1f77b4\")\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration', color=\"#ff7f0e\")\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_85ff0_row0_col0, #T_85ff0_row0_col1, #T_85ff0_row0_col2, #T_85ff0_row0_col3, #T_85ff0_row0_col4, #T_85ff0_row0_col5, #T_85ff0_row0_col6, #T_85ff0_row1_col0, #T_85ff0_row1_col1, #T_85ff0_row1_col2, #T_85ff0_row1_col3, #T_85ff0_row1_col4, #T_85ff0_row1_col6, #T_85ff0_row2_col0, #T_85ff0_row2_col1, #T_85ff0_row2_col2, #T_85ff0_row2_col3, #T_85ff0_row2_col4, #T_85ff0_row2_col5, #T_85ff0_row2_col6, #T_85ff0_row3_col0, #T_85ff0_row3_col1, #T_85ff0_row3_col2, #T_85ff0_row3_col3, #T_85ff0_row3_col4, #T_85ff0_row3_col5, #T_85ff0_row4_col0, #T_85ff0_row4_col1, #T_85ff0_row4_col2, #T_85ff0_row4_col3, #T_85ff0_row4_col6, #T_85ff0_row5_col0, #T_85ff0_row5_col1, #T_85ff0_row5_col2, #T_85ff0_row5_col3, #T_85ff0_row5_col4, #T_85ff0_row5_col5, #T_85ff0_row6_col0, #T_85ff0_row6_col1, #T_85ff0_row6_col2, #T_85ff0_row7_col0, #T_85ff0_row7_col1, #T_85ff0_row8_col0, #T_85ff0_row8_col1, #T_85ff0_row8_col3, #T_85ff0_row9_col0, #T_85ff0_row9_col1, #T_85ff0_row10_col1, #T_85ff0_row11_col1, #T_85ff0_row12_col0, #T_85ff0_row12_col1, #T_85ff0_row15_col0, #T_85ff0_row15_col5, #T_85ff0_row15_col6, #T_85ff0_row16_col5, #T_85ff0_row16_col6, #T_85ff0_row16_col7, #T_85ff0_row17_col4, #T_85ff0_row17_col5, #T_85ff0_row17_col6, #T_85ff0_row17_col7, #T_85ff0_row18_col1, #T_85ff0_row18_col4, #T_85ff0_row18_col5, #T_85ff0_row18_col6, #T_85ff0_row18_col7, #T_85ff0_row19_col0, #T_85ff0_row19_col1, #T_85ff0_row19_col3, #T_85ff0_row19_col4, #T_85ff0_row19_col5, #T_85ff0_row19_col6, #T_85ff0_row19_col7, #T_85ff0_row20_col0, #T_85ff0_row20_col1, #T_85ff0_row20_col3, #T_85ff0_row20_col4, #T_85ff0_row20_col5, #T_85ff0_row20_col6, #T_85ff0_row20_col7 {\n",
       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_85ff0_row0_col7, #T_85ff0_row7_col4, #T_85ff0_row7_col6 {\n",
       "  background-color: #ffd9d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row1_col5 {\n",
       "  background-color: #ff4545;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_85ff0_row1_col7, #T_85ff0_row3_col6 {\n",
       "  background-color: #ffa1a1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row2_col7, #T_85ff0_row13_col3 {\n",
       "  background-color: #ffc5c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row3_col7, #T_85ff0_row5_col7, #T_85ff0_row14_col6 {\n",
       "  background-color: #fffdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row4_col4 {\n",
       "  background-color: #ff9191;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row4_col5, #T_85ff0_row13_col1 {\n",
       "  background-color: #cdcdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row4_col7, #T_85ff0_row6_col5, #T_85ff0_row13_col7 {\n",
       "  background-color: #ddddff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row5_col6, #T_85ff0_row15_col4 {\n",
       "  background-color: #ffcdcd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row6_col3, #T_85ff0_row7_col2, #T_85ff0_row16_col2 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row6_col4, #T_85ff0_row7_col7, #T_85ff0_row8_col2, #T_85ff0_row8_col5 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row6_col6, #T_85ff0_row11_col7, #T_85ff0_row12_col3 {\n",
       "  background-color: #fff1f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row6_col7, #T_85ff0_row14_col3 {\n",
       "  background-color: #f9f9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row7_col3 {\n",
       "  background-color: #a9a9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row7_col5, #T_85ff0_row8_col6, #T_85ff0_row12_col2, #T_85ff0_row14_col1, #T_85ff0_row16_col1 {\n",
       "  background-color: #e5e5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row8_col4, #T_85ff0_row13_col2 {\n",
       "  background-color: #e1e1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row8_col7, #T_85ff0_row13_col6, #T_85ff0_row17_col2 {\n",
       "  background-color: #ffe5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row9_col2 {\n",
       "  background-color: #d1d1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row9_col3 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row9_col4 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row9_col5 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row9_col6, #T_85ff0_row10_col4, #T_85ff0_row11_col4, #T_85ff0_row15_col3, #T_85ff0_row16_col3 {\n",
       "  background-color: #ffc9c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row9_col7 {\n",
       "  background-color: #fff9f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row10_col0, #T_85ff0_row14_col0 {\n",
       "  background-color: #9999ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_85ff0_row10_col2 {\n",
       "  background-color: #bdbdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row10_col3, #T_85ff0_row12_col4 {\n",
       "  background-color: #ffbdbd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row10_col5, #T_85ff0_row17_col3 {\n",
       "  background-color: #ffe1e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row10_col6, #T_85ff0_row11_col5, #T_85ff0_row18_col3 {\n",
       "  background-color: #ffd1d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row10_col7, #T_85ff0_row11_col3, #T_85ff0_row14_col2, #T_85ff0_row15_col2, #T_85ff0_row15_col7, #T_85ff0_row19_col2 {\n",
       "  background-color: #fff5f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row11_col0 {\n",
       "  background-color: #7575ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_85ff0_row11_col2 {\n",
       "  background-color: #a1a1ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_85ff0_row11_col6 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row12_col5 {\n",
       "  background-color: #ff6565;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_85ff0_row12_col6 {\n",
       "  background-color: #ff8181;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_85ff0_row12_col7, #T_85ff0_row18_col2 {\n",
       "  background-color: #ededff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row13_col0 {\n",
       "  background-color: #8181ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_85ff0_row13_col4, #T_85ff0_row14_col4 {\n",
       "  background-color: #ffa5a5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row13_col5 {\n",
       "  background-color: #ff9595;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row14_col5 {\n",
       "  background-color: #ffb9b9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row14_col7 {\n",
       "  background-color: #ffdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row15_col1 {\n",
       "  background-color: #d9d9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row16_col0 {\n",
       "  background-color: #9d9dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_85ff0_row16_col4 {\n",
       "  background-color: #ff8d8d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row17_col0 {\n",
       "  background-color: #8d8dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_85ff0_row17_col1, #T_85ff0_row20_col2 {\n",
       "  background-color: #c5c5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_85ff0_row18_col0 {\n",
       "  background-color: #6565ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_85ff0_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th class=\"col_heading level0 col0\" >1</th>\n",
       "      <th class=\"col_heading level0 col1\" >2</th>\n",
       "      <th class=\"col_heading level0 col2\" >3</th>\n",
       "      <th class=\"col_heading level0 col3\" >5</th>\n",
       "      <th class=\"col_heading level0 col4\" >7</th>\n",
       "      <th class=\"col_heading level0 col5\" >8</th>\n",
       "      <th class=\"col_heading level0 col6\" >9</th>\n",
       "      <th class=\"col_heading level0 col7\" >10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row0\" class=\"row_heading level0 row0\" >0.36</th>\n",
       "      <td id=\"T_85ff0_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_85ff0_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_85ff0_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_85ff0_row0_col5\" class=\"data row0 col5\" ></td>\n",
       "      <td id=\"T_85ff0_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_85ff0_row0_col7\" class=\"data row0 col7\" >1.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row1\" class=\"row_heading level0 row1\" >0.51</th>\n",
       "      <td id=\"T_85ff0_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_85ff0_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_85ff0_row1_col4\" class=\"data row1 col4\" ></td>\n",
       "      <td id=\"T_85ff0_row1_col5\" class=\"data row1 col5\" >7.33%</td>\n",
       "      <td id=\"T_85ff0_row1_col6\" class=\"data row1 col6\" ></td>\n",
       "      <td id=\"T_85ff0_row1_col7\" class=\"data row1 col7\" >3.61%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row2\" class=\"row_heading level0 row2\" >0.71</th>\n",
       "      <td id=\"T_85ff0_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_85ff0_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_85ff0_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_85ff0_row2_col5\" class=\"data row2 col5\" ></td>\n",
       "      <td id=\"T_85ff0_row2_col6\" class=\"data row2 col6\" ></td>\n",
       "      <td id=\"T_85ff0_row2_col7\" class=\"data row2 col7\" >2.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row3\" class=\"row_heading level0 row3\" >1.0</th>\n",
       "      <td id=\"T_85ff0_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_85ff0_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_85ff0_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_85ff0_row3_col5\" class=\"data row3 col5\" ></td>\n",
       "      <td id=\"T_85ff0_row3_col6\" class=\"data row3 col6\" >3.61%</td>\n",
       "      <td id=\"T_85ff0_row3_col7\" class=\"data row3 col7\" >0.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row4\" class=\"row_heading level0 row4\" >1.4</th>\n",
       "      <td id=\"T_85ff0_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_85ff0_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_85ff0_row4_col4\" class=\"data row4 col4\" >4.27%</td>\n",
       "      <td id=\"T_85ff0_row4_col5\" class=\"data row4 col5\" >-2.02%</td>\n",
       "      <td id=\"T_85ff0_row4_col6\" class=\"data row4 col6\" ></td>\n",
       "      <td id=\"T_85ff0_row4_col7\" class=\"data row4 col7\" >-1.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row5\" class=\"row_heading level0 row5\" >1.96</th>\n",
       "      <td id=\"T_85ff0_row5_col0\" class=\"data row5 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_85ff0_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "      <td id=\"T_85ff0_row5_col4\" class=\"data row5 col4\" ></td>\n",
       "      <td id=\"T_85ff0_row5_col5\" class=\"data row5 col5\" ></td>\n",
       "      <td id=\"T_85ff0_row5_col6\" class=\"data row5 col6\" >1.95%</td>\n",
       "      <td id=\"T_85ff0_row5_col7\" class=\"data row5 col7\" >0.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row6\" class=\"row_heading level0 row6\" >2.74</th>\n",
       "      <td id=\"T_85ff0_row6_col0\" class=\"data row6 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_85ff0_row6_col3\" class=\"data row6 col3\" >-0.53%</td>\n",
       "      <td id=\"T_85ff0_row6_col4\" class=\"data row6 col4\" >0.71%</td>\n",
       "      <td id=\"T_85ff0_row6_col5\" class=\"data row6 col5\" >-1.28%</td>\n",
       "      <td id=\"T_85ff0_row6_col6\" class=\"data row6 col6\" >0.56%</td>\n",
       "      <td id=\"T_85ff0_row6_col7\" class=\"data row6 col7\" >-0.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row7\" class=\"row_heading level0 row7\" >3.84</th>\n",
       "      <td id=\"T_85ff0_row7_col0\" class=\"data row7 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row7_col2\" class=\"data row7 col2\" >-0.59%</td>\n",
       "      <td id=\"T_85ff0_row7_col3\" class=\"data row7 col3\" >-3.43%</td>\n",
       "      <td id=\"T_85ff0_row7_col4\" class=\"data row7 col4\" >1.47%</td>\n",
       "      <td id=\"T_85ff0_row7_col5\" class=\"data row7 col5\" >-0.97%</td>\n",
       "      <td id=\"T_85ff0_row7_col6\" class=\"data row7 col6\" >1.42%</td>\n",
       "      <td id=\"T_85ff0_row7_col7\" class=\"data row7 col7\" >0.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row8\" class=\"row_heading level0 row8\" >5.38</th>\n",
       "      <td id=\"T_85ff0_row8_col0\" class=\"data row8 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row8_col1\" class=\"data row8 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row8_col2\" class=\"data row8 col2\" >0.63%</td>\n",
       "      <td id=\"T_85ff0_row8_col3\" class=\"data row8 col3\" ></td>\n",
       "      <td id=\"T_85ff0_row8_col4\" class=\"data row8 col4\" >-1.21%</td>\n",
       "      <td id=\"T_85ff0_row8_col5\" class=\"data row8 col5\" >0.77%</td>\n",
       "      <td id=\"T_85ff0_row8_col6\" class=\"data row8 col6\" >-1.09%</td>\n",
       "      <td id=\"T_85ff0_row8_col7\" class=\"data row8 col7\" >0.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row9\" class=\"row_heading level0 row9\" >7.53</th>\n",
       "      <td id=\"T_85ff0_row9_col0\" class=\"data row9 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row9_col1\" class=\"data row9 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row9_col2\" class=\"data row9 col2\" >-1.82%</td>\n",
       "      <td id=\"T_85ff0_row9_col3\" class=\"data row9 col3\" >-0.92%</td>\n",
       "      <td id=\"T_85ff0_row9_col4\" class=\"data row9 col4\" >0.91%</td>\n",
       "      <td id=\"T_85ff0_row9_col5\" class=\"data row9 col5\" >-0.09%</td>\n",
       "      <td id=\"T_85ff0_row9_col6\" class=\"data row9 col6\" >2.04%</td>\n",
       "      <td id=\"T_85ff0_row9_col7\" class=\"data row9 col7\" >0.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row10\" class=\"row_heading level0 row10\" >10.54</th>\n",
       "      <td id=\"T_85ff0_row10_col0\" class=\"data row10 col0\" >-4.04%</td>\n",
       "      <td id=\"T_85ff0_row10_col1\" class=\"data row10 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row10_col2\" class=\"data row10 col2\" >-2.50%</td>\n",
       "      <td id=\"T_85ff0_row10_col3\" class=\"data row10 col3\" >2.50%</td>\n",
       "      <td id=\"T_85ff0_row10_col4\" class=\"data row10 col4\" >2.07%</td>\n",
       "      <td id=\"T_85ff0_row10_col5\" class=\"data row10 col5\" >1.12%</td>\n",
       "      <td id=\"T_85ff0_row10_col6\" class=\"data row10 col6\" >1.79%</td>\n",
       "      <td id=\"T_85ff0_row10_col7\" class=\"data row10 col7\" >0.34%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row11\" class=\"row_heading level0 row11\" >14.76</th>\n",
       "      <td id=\"T_85ff0_row11_col0\" class=\"data row11 col0\" >-5.43%</td>\n",
       "      <td id=\"T_85ff0_row11_col1\" class=\"data row11 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row11_col2\" class=\"data row11 col2\" >-3.63%</td>\n",
       "      <td id=\"T_85ff0_row11_col3\" class=\"data row11 col3\" >0.35%</td>\n",
       "      <td id=\"T_85ff0_row11_col4\" class=\"data row11 col4\" >2.06%</td>\n",
       "      <td id=\"T_85ff0_row11_col5\" class=\"data row11 col5\" >1.84%</td>\n",
       "      <td id=\"T_85ff0_row11_col6\" class=\"data row11 col6\" >-0.33%</td>\n",
       "      <td id=\"T_85ff0_row11_col7\" class=\"data row11 col7\" >0.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row12\" class=\"row_heading level0 row12\" >20.66</th>\n",
       "      <td id=\"T_85ff0_row12_col0\" class=\"data row12 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row12_col1\" class=\"data row12 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row12_col2\" class=\"data row12 col2\" >-0.97%</td>\n",
       "      <td id=\"T_85ff0_row12_col3\" class=\"data row12 col3\" >0.53%</td>\n",
       "      <td id=\"T_85ff0_row12_col4\" class=\"data row12 col4\" >2.52%</td>\n",
       "      <td id=\"T_85ff0_row12_col5\" class=\"data row12 col5\" >6.08%</td>\n",
       "      <td id=\"T_85ff0_row12_col6\" class=\"data row12 col6\" >4.88%</td>\n",
       "      <td id=\"T_85ff0_row12_col7\" class=\"data row12 col7\" >-0.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row13\" class=\"row_heading level0 row13\" >28.93</th>\n",
       "      <td id=\"T_85ff0_row13_col0\" class=\"data row13 col0\" >-4.97%</td>\n",
       "      <td id=\"T_85ff0_row13_col1\" class=\"data row13 col1\" >-1.88%</td>\n",
       "      <td id=\"T_85ff0_row13_col2\" class=\"data row13 col2\" >-1.20%</td>\n",
       "      <td id=\"T_85ff0_row13_col3\" class=\"data row13 col3\" >2.20%</td>\n",
       "      <td id=\"T_85ff0_row13_col4\" class=\"data row13 col4\" >3.47%</td>\n",
       "      <td id=\"T_85ff0_row13_col5\" class=\"data row13 col5\" >4.14%</td>\n",
       "      <td id=\"T_85ff0_row13_col6\" class=\"data row13 col6\" >0.95%</td>\n",
       "      <td id=\"T_85ff0_row13_col7\" class=\"data row13 col7\" >-1.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row14\" class=\"row_heading level0 row14\" >40.5</th>\n",
       "      <td id=\"T_85ff0_row14_col0\" class=\"data row14 col0\" >-3.91%</td>\n",
       "      <td id=\"T_85ff0_row14_col1\" class=\"data row14 col1\" >-0.96%</td>\n",
       "      <td id=\"T_85ff0_row14_col2\" class=\"data row14 col2\" >0.40%</td>\n",
       "      <td id=\"T_85ff0_row14_col3\" class=\"data row14 col3\" >-0.26%</td>\n",
       "      <td id=\"T_85ff0_row14_col4\" class=\"data row14 col4\" >3.53%</td>\n",
       "      <td id=\"T_85ff0_row14_col5\" class=\"data row14 col5\" >2.72%</td>\n",
       "      <td id=\"T_85ff0_row14_col6\" class=\"data row14 col6\" >0.07%</td>\n",
       "      <td id=\"T_85ff0_row14_col7\" class=\"data row14 col7\" >1.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row15\" class=\"row_heading level0 row15\" >56.69</th>\n",
       "      <td id=\"T_85ff0_row15_col0\" class=\"data row15 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row15_col1\" class=\"data row15 col1\" >-1.48%</td>\n",
       "      <td id=\"T_85ff0_row15_col2\" class=\"data row15 col2\" >0.34%</td>\n",
       "      <td id=\"T_85ff0_row15_col3\" class=\"data row15 col3\" >2.06%</td>\n",
       "      <td id=\"T_85ff0_row15_col4\" class=\"data row15 col4\" >1.90%</td>\n",
       "      <td id=\"T_85ff0_row15_col5\" class=\"data row15 col5\" ></td>\n",
       "      <td id=\"T_85ff0_row15_col6\" class=\"data row15 col6\" ></td>\n",
       "      <td id=\"T_85ff0_row15_col7\" class=\"data row15 col7\" >0.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row16\" class=\"row_heading level0 row16\" >79.37</th>\n",
       "      <td id=\"T_85ff0_row16_col0\" class=\"data row16 col0\" >-3.81%</td>\n",
       "      <td id=\"T_85ff0_row16_col1\" class=\"data row16 col1\" >-1.07%</td>\n",
       "      <td id=\"T_85ff0_row16_col2\" class=\"data row16 col2\" >-0.51%</td>\n",
       "      <td id=\"T_85ff0_row16_col3\" class=\"data row16 col3\" >2.18%</td>\n",
       "      <td id=\"T_85ff0_row16_col4\" class=\"data row16 col4\" >4.52%</td>\n",
       "      <td id=\"T_85ff0_row16_col5\" class=\"data row16 col5\" ></td>\n",
       "      <td id=\"T_85ff0_row16_col6\" class=\"data row16 col6\" ></td>\n",
       "      <td id=\"T_85ff0_row16_col7\" class=\"data row16 col7\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row17\" class=\"row_heading level0 row17\" >111.12</th>\n",
       "      <td id=\"T_85ff0_row17_col0\" class=\"data row17 col0\" >-4.50%</td>\n",
       "      <td id=\"T_85ff0_row17_col1\" class=\"data row17 col1\" >-2.25%</td>\n",
       "      <td id=\"T_85ff0_row17_col2\" class=\"data row17 col2\" >1.02%</td>\n",
       "      <td id=\"T_85ff0_row17_col3\" class=\"data row17 col3\" >1.11%</td>\n",
       "      <td id=\"T_85ff0_row17_col4\" class=\"data row17 col4\" ></td>\n",
       "      <td id=\"T_85ff0_row17_col5\" class=\"data row17 col5\" ></td>\n",
       "      <td id=\"T_85ff0_row17_col6\" class=\"data row17 col6\" ></td>\n",
       "      <td id=\"T_85ff0_row17_col7\" class=\"data row17 col7\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row18\" class=\"row_heading level0 row18\" >155.57</th>\n",
       "      <td id=\"T_85ff0_row18_col0\" class=\"data row18 col0\" >-6.07%</td>\n",
       "      <td id=\"T_85ff0_row18_col1\" class=\"data row18 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row18_col2\" class=\"data row18 col2\" >-0.71%</td>\n",
       "      <td id=\"T_85ff0_row18_col3\" class=\"data row18 col3\" >1.85%</td>\n",
       "      <td id=\"T_85ff0_row18_col4\" class=\"data row18 col4\" ></td>\n",
       "      <td id=\"T_85ff0_row18_col5\" class=\"data row18 col5\" ></td>\n",
       "      <td id=\"T_85ff0_row18_col6\" class=\"data row18 col6\" ></td>\n",
       "      <td id=\"T_85ff0_row18_col7\" class=\"data row18 col7\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row19\" class=\"row_heading level0 row19\" >217.8</th>\n",
       "      <td id=\"T_85ff0_row19_col0\" class=\"data row19 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row19_col1\" class=\"data row19 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row19_col2\" class=\"data row19 col2\" >0.39%</td>\n",
       "      <td id=\"T_85ff0_row19_col3\" class=\"data row19 col3\" ></td>\n",
       "      <td id=\"T_85ff0_row19_col4\" class=\"data row19 col4\" ></td>\n",
       "      <td id=\"T_85ff0_row19_col5\" class=\"data row19 col5\" ></td>\n",
       "      <td id=\"T_85ff0_row19_col6\" class=\"data row19 col6\" ></td>\n",
       "      <td id=\"T_85ff0_row19_col7\" class=\"data row19 col7\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85ff0_level0_row20\" class=\"row_heading level0 row20\" >304.91</th>\n",
       "      <td id=\"T_85ff0_row20_col0\" class=\"data row20 col0\" ></td>\n",
       "      <td id=\"T_85ff0_row20_col1\" class=\"data row20 col1\" ></td>\n",
       "      <td id=\"T_85ff0_row20_col2\" class=\"data row20 col2\" >-2.19%</td>\n",
       "      <td id=\"T_85ff0_row20_col3\" class=\"data row20 col3\" ></td>\n",
       "      <td id=\"T_85ff0_row20_col4\" class=\"data row20 col4\" ></td>\n",
       "      <td id=\"T_85ff0_row20_col5\" class=\"data row20 col5\" ></td>\n",
       "      <td id=\"T_85ff0_row20_col6\" class=\"data row20 col6\" ></td>\n",
       "      <td id=\"T_85ff0_row20_col7\" class=\"data row20 col7\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x29b5bb280>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric_count = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('count').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "n = len(dataset)\n",
    "bins = len(B_W_Metric)\n",
    "B_W_Metric_pivot = B_W_Metric[B_W_Metric_count['p'] > max(50, n / (3 * bins))].pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='d_bin'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "B_W_Metric_raw.groupby(by=['d_bin']).agg('count').reset_index().plot.bar(x='d_bin', y='p', legend=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsrs4anki",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
